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Deep learning frameworks for enhanced user engagement prediction
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Resumo(s)
This work demonstrates the individual contribution of Zhanshuo Guo in the field lab project “How Early Can We Predict Churn? Short-Window Engagement Forecasting in a Hybrid UGC Music Platform”. While chapters 1-3 summarize the collective effort of the group,
chapter 4 dives into the deep learning approaches of the project. Using impression-level data from NetEase Cloud Music recently launched UGC module “Cloud Village”, this work aims at providing a scalable solution for predicting user engagement from short-term behaviors, which can help the management team to handle high user mobility issue and design win-back strategies. We employ both machine and deep learning models across two targets: binary churn and multiclass engagement. The cross design provides complementary perspectives on user behaviors, enabling performance indicators from one task to enrich the other. The result contributes to a more robust understanding of engagement prediction and operational values.
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Deep learning Churn prediction Long short-term emory Sequence processing